Is my company’s data valuable to AI companies?

By Cody Houser, Founder · Last updated October 24, 2026

The short answer: it’s most likely to be valuable if your company has 50 or more desk and office staff, at least three years of history, and a complete record across five core systems: email, file storage, internal chat, CRM, and accounting. Long, unbroken history in the same systems is worth more than a record split across frequent tool changes.

Why real operational data matters to AI

Demand for company data emerged as AI labs hit the limits of public and synthetic datasets, according to MIT Technology Review. One buyer, Surge AI, uses archives to build “reinforcement-learning gyms” where AI agents practice tasks, and looks for records that capture disagreements and tricky judgment calls. Real archives also avoid errors common in synthetic worlds, such as references to dates that don’t exist.

The target is agents that do real jobs. micro1 uses de-identified company data to build environments where models “learn to work through workflows, make decisions and complete complex tasks,” because “real business operations involve incomplete information, competing priorities and exceptions that require judgment” (The Next Web). Operational records are “particularly valuable for developing AI agents designed to perform real workplace tasks” (CX Today). Labelbox’s Prism calls authentic operational data “the next bottleneck in AI” (Prism).

Operating companies are now getting the calls closed startups got first, and the law firm Frankfurt Kurnit Klein & Selz says it’s fielding client questions about whether they can say yes. The biggest public example is Google’s $10 million winning bid for Spirit Airlines’ data. One caveat: no company has yet published tests comparing training on real-world data with the alternatives (MIT Technology Review).

What AI labs and data buyers say they want

Three buyers publish their wish lists. In their own words:

micro1: data “that reflects how work is actually performed,” including SOPs, process documentation, internal knowledge bases, workflows, and templates. It lists examples by function: engineering workflows and bug tracking; financial processes, reconciliations, and approvals; support tickets and QA processes; CRM data and sales playbooks; contract workflows and compliance procedures; and logistics and fulfillment SOPs. It wants several years of records across multiple systems, 30+ full-time employees, and English-language data.
Labelbox Prism: “operational workflows across teams and functions, human decisions and escalation paths, expert reviews and corrections,” internal tool usage logs, and “edge cases and real-world failure modes.” Categories it marks urgent include financial reasoning traces (underwriting, valuation, M&A) and cross-system Salesforce, Jira, and Slack workflows with timestamps, user roles, and causal sequence. It also lists six-plus months of multi-channel support logs and full IDE coding sessions.
Nyne: three categories: finance and accounting (invoices, estimates, AP and AR, and transaction history), customer records (CRM, service histories, support tickets), and scheduling and delivery (jobs, dispatch, project records). Its program targets companies with at least 20 full-time US W-2 employees and three years in business.

The common thread is the record of work moving from start to finish, with the people, decisions, and exceptions along the way. To compare buyer types, see the AI training data marketplace guide.

The checklist

The more of these you can answer yes to, the stronger your position:

Check	What buyers look for
Desk and office staff	50 or more (100+ is stronger)
Operating history	3 years minimum (5+ is stronger)
Email	Company-wide mailboxes, with history
File storage	Shared drives with real working documents
Internal chat	Slack or Teams history across channels
CRM	Contacts, deals, and activity logged over time
Accounting	Books kept in an accounting system for the operating years
Language	Internal communication mainly in English
Rights	The company owns the records and can share them

Count desk and office staff rather than total headcount: the records come from people who work in email, documents, and chat. A standalone codebase with real value can be the exception to the five-system rule; one founder netted about $25,000 for a startup’s code base (MIT Technology Review).

System by system: what makes each more or less valuable

Email: worth more when full mailboxes across the team go back years, with threads and attachments intact. Worth less when it’s been purged, or when much of it is other companies’ confidential material under NDA, which you may not have the right to share (FKKS).
File storage: working drives such as Google Drive, SharePoint, or Dropbox are worth more with drafts and version history, not just final PDFs; the Spirit Airlines archive listed contracts with draft-to-final versions. Third-party copyrighted material in shared files can carry restrictions (FKKS).
Internal chat: Slack or Teams history kept across channels, rather than auto-deleted. Depth and variety matter: a buyer told MIT Technology Review that one company’s Slack can outweigh another’s Slack, email, and calendars combined. How you export it matters too: Slack’s API terms bar third-party app providers from using API data to train a large language model (FKKS).
CRM and support: contacts, deals, notes, and tickets logged over years, ideally with resolution outcomes. Prism lists six-plus months of support logs with resolution tags as high demand. Data linking CRM activity to tickets and chat is worth more than any one system alone.
Accounting: books in a system such as QuickBooks, Xero, or NetSuite, with invoices, AP and AR, and transactions through the operating years (Nyne). Payroll, tax, and benefits records are a different matter; FKKS lists them among data to consider excluding (FKKS).

The ideal is the full history of one company, in its original systems, with nothing purged.

What lowers the value

Fewer than 50 desk and office staff, or less than three years of history
One or more of the five core systems missing
Frequent tool switches that break the record into short fragments
Auto-delete policies that have removed chat or email history
Internal communication mainly in a language other than English
Material that has to be excluded anyway, such as privileged communications, HR files, and health information

Red flags that can rule a company out

Data you don’t have the right to share: client-owned material, privileged communications, or records restricted by contract. In the Spirit sale, a software vendor objected that records its platform generated belong to the vendor (FKKS). See the law firm and accounting firm guides.
Privacy promises that don’t allow it: you’re bound by the privacy statements in effect when data was collected, and most employee notices don’t contemplate selling workplace communications for AI training (FKKS). See whether companies can sell employee data.
Sanctioned or trade-restricted locations: otherwise, location is generally open
Healthcare and patient records: considered case by case, if at all

Closed and bankrupt companies count too

A company that has wound down can qualify, as long as the data still exists and the company or its estate holds the rights to it. A closed company’s record is often complete. See licensing data when your business is closing.

Before you talk to a buyer

Don’t purge old systems or let accounts lapse until you know whether the records have value
Write down which systems you use, since when, and roughly how many people use each
Confirm who in the company can approve a deal like this, and bring in legal, privacy, and HR early
Don’t send files or samples; a description is enough to start, and a sample can itself be a disclosure (FKKS)

No checklist can tell you a price: there’s no standard one, and an assessment doesn’t guarantee an offer. For published ranges, see how much company data is worth. If you can answer yes to most of the checks above, it’s worth finding out. For the full process, read how to sell data to AI companies.

Questions people ask

Is my company data valuable to AI companies?

It’s most likely to be if you have 50 or more desk and office staff, at least three years of history, and a complete record across email, file storage, internal chat, CRM, and accounting, with clear rights to share it. Buyers say depth, quality, and how authentically the records capture real work matter more than raw volume.

What data do AI companies want from businesses?

Buyers’ published lists center on records of how work actually gets done: SOPs and process documentation, internal communications, CRM and support histories, project and ticket records, accounting transactions, decision and escalation paths, expert reviews and corrections, and data that links activity across several systems.

What data do AI labs buy?

Labs and the data companies that supply them buy archives from closed startups, operational data licensed by operating companies, and in Spirit Airlines’ case, a bankrupt airline’s enterprise records. Buyers include foundation AI labs, vendors that build simulated workspaces for AI training, and tech companies building their own models.

Is a small company’s data worth anything?

Sometimes. Published eligibility floors vary: micro1 lists 30+ full-time employees and Nyne lists at least 20 full-time US W-2 employees and three years in business. Closed startups have sold code and workspace data too. Smaller records are a weaker fit for buyers that want years of history across several systems.

Does having more data make it more valuable?

Not automatically. Nyne says more records don’t automatically mean a higher offer, and Labelbox says value is driven less by volume than by how authentically the data captures complex, real-world work. One AI data buyer told MIT Technology Review a single deep Slack history can be worth more than another company’s Slack, email, and calendars combined.

Sources

This guide is general information, not legal, tax, or financial advice. Review any proposed agreement with your own advisers.

Related guides

Check whether your company is a fit. Start with a description of your records; no files needed.